CN113176584A

CN113176584A - Resolving range-rate ambiguity in sensor echoes

Info

Publication number: CN113176584A
Application number: CN202110096973.XA
Authority: CN
Inventors: G·奥兹比尔金
Original assignee: Motional AD LLC
Current assignee: Motional AD LLC
Priority date: 2020-01-24
Filing date: 2021-01-25
Publication date: 2021-07-27
Also published as: US20210229662A1; GB202100844D0; GB2596376A; US11814044B2; KR20210095803A; GB2596376B; DE102021101206A1

Abstract

The invention relates to a solution to range-rate ambiguity in sensor echoes. One method comprises the following steps: transmitting a first transmission signal corresponding to a first range-rate window size; receiving a first received signal; determining a first detection range rate of the object based on the first received signal; transmitting a second transmission signal corresponding to a second range-rate window size; receiving a second received signal; determining a second detection range rate of the object based on the second received signal; computing a first range-rate window index based on the first range-rate window index difference; in accordance with a determination that the first range-rate window index satisfies a predefined criterion, calculating an estimated range-rate based on the first range-rate window index difference; and in accordance with a determination that the first range-rate window index does not satisfy a predefined criterion, forgoing calculation of the estimated range-rates based on the first range-rate window index difference.

Description

Resolving range-rate ambiguity in sensor echoes

Technical Field

This description relates to detecting objects with sensors (e.g., RADAR), and in particular, to resolving range rate ambiguity in received signals (e.g., for use in navigating an autonomous vehicle).

Background

Autonomous vehicles may be used to transport people and/or cargo (e.g., packages, objects, or other items) from one location to another. For example, the autonomous vehicle may navigate to a location of a person, wait for the person to board the autonomous vehicle, and navigate to a specified destination (e.g., a location selected by the person). For navigation in the environment, these autonomous vehicles are equipped with various sensors to detect surrounding objects.

Disclosure of Invention

The subject matter described in this specification is directed to a computer system and techniques for detecting objects in an environment surrounding an autonomous vehicle. Generally, a computer system is configured to receive input from one or more sensors of a vehicle, detect one or more objects in an environment surrounding the vehicle based on the received input, and operate the vehicle based on the detection of the objects.

For example, for a pair of sensor measurements each having a potential ambiguity range rate value, a window index is calculated for the range-rate window index difference based on the detection range rate of the measurement and the range-rate window size associated with the measurement. If the computed window index satisfies a predefined criterion (e.g., the window index is close to an integer value of-1, 0, or 1), then range-rates are computed based on the range-rate window index differences.

In some embodiments, a method comprises: transmitting, using one or more transmitters, a first transmission signal into an environment, the first transmission signal corresponding to a first range-rate window size; receiving, using one or more receivers, a first received signal comprising at least a portion of the first transmitted signal that has been reflected from an object in the environment; determining, using a processing circuit, a first detection range rate of the object based on the first received signal; transmitting, using the one or more transmitters, a second transmission signal into the environment after transmitting the first transmission signal, the second transmission signal corresponding to a second range rate window size; receiving, using the one or more receivers, a second received signal comprising at least a portion of the second transmitted signal that has been reflected from the object in the environment; determining, using the processing circuit, a second detection range rate of the object based on the second received signal; calculating, using the processing circuit, a first range-rate window index based on a first range-rate window index difference, the first detection range rate, the second detection range rate, the first range-rate window size, and the second range-rate window size; in accordance with a determination that the first range-rate window index satisfies a predefined criterion, calculating, using the processing circuitry, an estimated range-rate based on the first range-rate window index difference; and in accordance with a determination that the first range-rate window index does not satisfy the predefined criteria, forgoing calculation of the estimated range-rates based on the first range-rate window index difference.

In some embodiments, a system comprises: one or more computer processors; and one or more storage media storing instructions that, when executed by the one or more computer processors, cause performance of the above-described methods.

In some embodiments, a storage medium stores instructions that, when executed by one or more computing devices, cause performance of the above-described method.

These and other aspects, features and implementations may be expressed as methods, apparatus, systems, components, program products, methods or steps for performing functions, and in other ways.

These and other aspects, features and implementations will become apparent from the following description, including the claims.

Drawings

Fig. 1 illustrates an example of an autonomous vehicle having autonomous capabilities.

FIG. 2 illustrates an example "cloud" computing environment.

FIG. 3 illustrates a computer system.

Fig. 4 illustrates an example architecture of an autonomous vehicle.

FIG. 5 shows an example of inputs and outputs that may be used by the perception module.

FIG. 6 shows an example of a LiDAR system.

FIG. 7 shows the LiDAR system in operation.

FIG. 8 shows additional details of the operation of a LiDAR system.

FIG. 9 shows a block diagram of the relationship between inputs and outputs of a planning module.

Fig. 10 shows a directed graph used in path planning.

FIG. 11 shows a block diagram of the inputs and outputs of the control module.

FIG. 12 shows a block diagram of the inputs, outputs and components of the controller.

Fig. 13A-13B illustrate examples of sensor signals.

FIG. 14A illustrates an example of an environment including physical objects that may be detected by a sensor.

FIG. 14B shows a range-rate window corresponding to the first signal of the sensor.

FIG. 14C shows a range-rate window corresponding to the second signal of the sensor.

FIG. 15 is a flow diagram of an example process for detecting objects in an environment and operating a vehicle based on detection results of the objects.

Detailed Description

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.

In the drawings, the specific arrangement or order of schematic elements (such as those representing devices, modules, instruction blocks, and data elements) is shown for ease of description. However, those skilled in the art will appreciate that the particular order or arrangement of the elements illustrated in the drawings is not intended to imply that a particular order or sequence of processing, or separation of processes, is required. Moreover, the inclusion of schematic elements in the figures is not intended to imply that such elements are required in all embodiments, nor that the features represented by such elements are necessarily included or combined with other elements in some embodiments.

Further, in the drawings, a connecting element, such as a solid or dashed line or arrow, is used to illustrate a connection, relationship or association between two or more other illustrated elements, and the absence of any such connecting element is not intended to imply that a connection, relationship or association cannot exist. In other words, connections, relationships, or associations between some elements are not shown in the drawings so as not to obscure the disclosure. Further, for ease of illustration, a single connected element is used to represent multiple connections, relationships, or associations between elements. For example, if a connection element represents a communication of signals, data, or instructions, those skilled in the art will appreciate that such element represents one or more signal paths (e.g., buses) that may be required to affect the communication.

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments described. It will be apparent, however, to one skilled in the art that the various embodiments described may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail as not to unnecessarily obscure aspects of the embodiments.

Several features described below can each be used independently of one another or with any combination of the other features. However, any individual feature may not solve any of the problems discussed above, or may only solve one of the problems discussed above. Some of the problems discussed above may not be adequately addressed by any of the features described herein. Although headings are provided, information related to a particular heading, but not found in the section having that heading, may also be found elsewhere in this specification. The examples are described herein according to the following summary:

1. general overview

2. Overview of hardware

3. Autonomous vehicle architecture

4. Autonomous vehicle input

5. Autonomous vehicle planning

6. Autonomous vehicle control

7. Computing system for object detection using pillars (pilars)

8. Example Point clouds and columns

9. Example Process for detecting an object and operating a vehicle based on the detection of the object

General overview

Autonomous vehicles driven in complex environments (e.g., urban environments) present significant technical challenges. In order for an autonomous vehicle to navigate in these environments, the vehicle uses sensors such as LIDAR or RADAR to detect various objects such as vehicles, pedestrians, and bicycles in real time. One method for performing object detection includes determining a range rate at which objects are detected. However, some sensors (such as pulsed RADAR) have potential ambiguity in the range rate they can detect. For example, a sensor may be able to detect v_minAnd v_maxThe rate of change of distance therebetween. Since the window of range rates that the sensor can detect is limited, there is an actual range rate v_maxObject of + x will be detected by the sensor as having v_minRange-rate of + x (assuming x is less than v)_maxAnd v_minThe difference therebetween). In this case, v_maxThe range rate of + x is considered to be "fold" to the window (v)_min，v_max) In (1). Described herein are techniques for resolving ambiguity in detection range rates caused by objects having range rates outside of a range rate window of a sensor. The process of resolving range-rate ambiguity is referred to herein as "unfolding".

Current techniques for addressing the ambiguity range rate are computationally inefficient. The disclosed embodiments include systems and techniques for efficiently and quickly resolving range-rate ambiguities (e.g., unfolding RADAR echoes) of a test object based on sensor inputs. For example, for a pair of sensor measurements each having a potential ambiguity range rate value, a window index is calculated for the range-rate window index difference based on the detection range rate of the measurement and the range-rate window size associated with the measurement. If the window index satisfies a predefined criterion (e.g., the window index is close to an integer value of-1, 0, or 1), the range rate is calculated based on the window index. An advantage of the present technique is that it can be performed efficiently outside of the target tracker.

Overview of hardware

Fig. 1 shows an example of an autonomous vehicle 100 with autonomous capabilities.

As used herein, the term "autonomous capability" refers to a function, feature, or facility that enables a vehicle to operate partially or fully without real-time human intervention, including, but not limited to, fully autonomous vehicles, highly autonomous vehicles, and conditional autonomous vehicles.

As used herein, an Autonomous Vehicle (AV) is a vehicle with autonomous capabilities.

As used herein, "vehicle" includes a means of transportation for cargo or personnel. Such as cars, buses, trains, airplanes, drones, trucks, boats, ships, submarines, airships, etc. An unmanned car is an example of a vehicle.

As used herein, "trajectory" refers to a path or route that navigates an AV from a first spatiotemporal location to a second spatiotemporal location. In an embodiment, the first spatiotemporal location is referred to as an initial location or a starting location and the second spatiotemporal location is referred to as a destination, a final location, a target location, or a target location. In some examples, a track consists of one or more road segments (e.g., segments of a road), and each road segment consists of one or more blocks (e.g., a portion of a lane or intersection). In an embodiment, the spatiotemporal locations correspond to real-world locations. For example, the space-time location is a boarding or alighting location to allow people or cargo to board or disembark.

As used herein, a "sensor(s)" includes one or more hardware components for detecting information related to the environment surrounding the sensor. Some hardware components may include sensing components (e.g., image sensors, biometric sensors), transmitting and/or receiving components (e.g., laser or radio frequency wave transmitters and receivers), electronic components (such as analog-to-digital converters), data storage devices (such as RAM and/or non-volatile memory), software or firmware components and data processing components (such as application specific integrated circuits), microprocessors and/or microcontrollers.

As used herein, a "scene description" is a data structure (e.g., a list) or data stream that includes one or more classified or tagged objects detected by one or more sensors on an AV vehicle, or one or more classified or tagged objects provided by a source external to the AV.

As used herein, a "roadway" is a physical area that can be traversed by a vehicle and may correspond to a named corridor (e.g., a city street, an interstate highway, etc.) or may correspond to an unnamed corridor (e.g., a lane of travel within a house or office building, a segment of a parking lot, a segment of an empty parking lot, a dirt passageway in a rural area, etc.). Because some vehicles (e.g., four-wheel drive trucks, off-road vehicles (SUVs), etc.) are able to traverse a variety of physical areas not particularly suited for vehicle travel, a "road" may be any physical area that a municipality or other government or administrative authority has not formally defined as a passageway.

As used herein, a "lane" is a portion of a roadway that may be traversed by a vehicle, and may correspond to most or all of the space between lane markings, or only some of the space between lane markings (e.g., less than 50%). For example, a roadway with far apart lane markers may accommodate two or more vehicles between the markers such that one vehicle may pass over another without crossing the lane markers, and thus may be interpreted as having lanes narrower than the space between the lane markers, or having two lanes between the lanes. In the absence of lane markings, the lane may also be interpreted. For example, lanes may be defined based on physical characteristics of the environment (e.g., rocks and trees along a passageway in a rural area).

"one or more" includes a function performed by one element, a function performed by multiple elements, e.g., in a distributed fashion, several functions performed by one element, several functions performed by several elements, or any combination thereof.

It will also be understood that, although the terms "first," "second," and the like may be used herein to describe various elements in some cases, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact, without departing from the scope of the various described embodiments. Both the first contact and the second contact are contacts, but they are not identical contacts unless otherwise stated.

The terminology used in the description of the various embodiments described herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various embodiments described and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that "and/or" as used herein refers to and includes any and all possible combinations of one or more related inventory items. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

As used herein, the term "if" is optionally understood to mean "when" or "at the time" or "in response to a determination of" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if [ stated condition or event ] has been detected" is optionally understood to mean "upon determination" or "in response to a determination" or "upon detection of [ stated condition or event ] or" in response to detection of [ stated condition or event ] ", depending on the context.

As used herein, an AV system refers to AV and to an array of hardware, software, stored data, and real-time generated data that support AV operations. In an embodiment, the AV system is incorporated within the AV. In an embodiment, the AV system is distributed across several sites. For example, some software of the AV system is implemented in a cloud computing environment similar to the cloud computing environment 200 described below with respect to fig. 2.

In general, this document describes techniques applicable to any vehicle having one or more autonomous capabilities, including fully autonomous vehicles, highly autonomous vehicles, and conditional autonomous vehicles, such as so-called class 5, class 4, and class 3 vehicles, respectively (see SAE International Standard J3016: Classification and definition of terms related to automotive autonomous systems on roads, the entire contents of which are incorporated by reference into this document for more detailed information on the level of autonomy of the vehicle). The technology described in this document is also applicable to partly autonomous vehicles and driver-assisted vehicles, such as so-called class 2 and class 1 vehicles (see SAE international standard J3016: classification and definition of terms relating to automotive autonomous systems on roads). In an embodiment, one or more of the class 1, class 2, class 3, class 4, and class 5 vehicle systems may automatically perform certain vehicle operations (e.g., steering, braking, and map usage) under certain operating conditions based on processing of sensor inputs. The technology described in this document may benefit any class of vehicles ranging from fully autonomous vehicles to vehicles operated by humans.

Referring to fig. 1, the AV system 120 operates the AV100 along a trajectory 198, through the environment 190 to a destination 199 (sometimes referred to as a final location), while avoiding objects (e.g., natural obstacles 191, vehicles 193, pedestrians 192, riders, and other obstacles) and complying with road rules (e.g., operational rules or driving preferences).

In an embodiment, the AV system 120 includes a device 101 equipped to receive and operate operation commands from the computer processor 146. In an embodiment, the calculation processor 146 is similar to the processor 304 described below with reference to fig. 3. Examples of devices 101 include a steering controller 102, a brake 103, a gear, an accelerator pedal or other acceleration control mechanism, windshield wipers, side door locks, window controls, and steering indicators.

In an embodiment, the AV system 120 includes sensors 121 for measuring or inferring attributes of the state or condition of the AV100, such as the location, linear and angular velocities and accelerations, and heading (e.g., direction of the front end of the AV 100) of the AV. Examples of sensors 121 are GPS, Inertial Measurement Units (IMU) that measure both linear acceleration and angular velocity of the vehicle, wheel velocity sensors for measuring or estimating wheel slip rates, wheel brake pressure or torque sensors, engine torque or wheel torque sensors, and steering angle and angular velocity sensors.

In an embodiment, the sensors 121 further comprise sensors for sensing or measuring properties of the environment of the AV. Such as a monocular or stereo camera 122 for the visible, infrared, or thermal (or both) spectrum, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight (TOF) depth sensors, rate sensors, temperature sensors, humidity sensors, and precipitation sensors.

In an embodiment, the AV system 120 includes a data storage unit 142 and a memory 144 for storing machine instructions associated with a computer processor 146 or data collected by the sensors 121. In an embodiment, the data storage unit 142 is similar to the ROM 308 or the storage device 310 described below with respect to fig. 3. In an embodiment, memory 144 is similar to main memory 306 described below. In an embodiment, data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates, or weather conditions. In an embodiment, data related to the environment 190 is transmitted from the remote database 134 to the AV100 over a communication channel.

In an embodiment, the AV system 120 includes a communication device 140 for communicating to the AV100 attributes measured or inferred for the state and conditions of other vehicles, such as position, linear and angular velocities, linear and angular accelerations, and linear and angular headings. These devices include vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication devices as well as devices for wireless communication over point-to-point or ad hoc (ad hoc) networks or both. In an embodiment, the communication devices 140 communicate across the electromagnetic spectrum (including radio and optical communications) or other media (e.g., air and acoustic media). The combination of vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) communications (and in some embodiments one or more other types of communications) is sometimes referred to as vehicle-to-everything (V2X) communications. The V2X communications are generally compliant with one or more communication standards for communications with and between autonomous vehicles.

In an embodiment, the communication device 140 comprises a communication interface. Such as a wired, wireless, WiMAX, WiFi, bluetooth, satellite, cellular, optical, near field, infrared, or radio interface. The communication interface transmits data from the remote database 134 to the AV system 120. In an embodiment, remote database 134 is embedded in cloud computing environment 200 as described in fig. 2. The communication interface 140 transmits data collected from the sensors 121 or other data related to the operation of the AV100 to the remote database 134. In an embodiment, the communication interface 140 transmits teleoperation-related information to the AV 100. In some embodiments, the AV100 communicates with other remote (e.g., "cloud") servers 136.

In an embodiment, the remote database 134 also stores and transmits digital data (e.g., stores data such as road and street locations). These data are stored in memory 144 on AV100 or transmitted from remote database 134 to AV100 over a communications channel.

In an embodiment, the remote database 134 stores and transmits historical information (e.g., velocity and acceleration profiles) related to driving attributes of vehicles that previously traveled along the trajectory 198 at similar times of the day. In one implementation, such data may be stored in memory 144 on AV100 or transmitted from remote database 134 to AV100 over a communications channel.

A computing device 146 located on the AV100 algorithmically generates control actions based on both real-time sensor data and a priori information, allowing the AV system 120 to perform its autonomous driving capabilities.

In an embodiment, the AV system 120 includes a computer peripheral 132 coupled to a computing device 146 for providing information and reminders to and receiving input from a user (e.g., an occupant or remote user) of the AV 100. In an embodiment, peripheral 132 is similar to display 312, input device 314, and cursor controller 316 discussed below with reference to fig. 3. The coupling is wireless or wired. Any two or more of the interface devices may be integrated into a single device.

FIG. 2 illustrates an example "cloud" computing environment. Cloud computing is a service delivery model for enabling convenient, on-demand access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) over a network. In a typical cloud computing system, one or more large cloud data centers house machines for delivering services provided by the cloud. Referring now to fig. 2, cloud computing environment 200 includes

cloud data centers

204a, 204b, and 204c interconnected by cloud 202.

Data centers

204a, 204b, and 204c provide cloud computing services for

computer systems

206a, 206b, 206c, 206d, 206e, and 206f connected to cloud 202.

Cloud computing environment 200 includes one or more cloud data centers. In general, a cloud data center (e.g., cloud data center 204a shown in fig. 2) refers to a physical arrangement of servers that make up a cloud (e.g., cloud 202 shown in fig. 2 or a particular portion of a cloud). For example, the servers are physically arranged in rooms, groups, rows, and racks in a cloud data center. The cloud data center has one or more zones, including one or more server rooms. There are one or more rows of servers per room, and each row includes one or more racks. Each rack includes one or more individual server nodes. In some implementations, servers in a region, room, rack, and/or row are arranged into groups based on physical infrastructure requirements (including electrical, energy, thermal, heat, and/or other requirements) of the data center facility. In an embodiment, the server node is similar to the computer system described in FIG. 3. Data center 204a has a number of computing systems distributed across multiple racks.

Cloud 202 includes

cloud data centers

204a, 204b, and 204c and network resources (e.g., network devices, nodes, routers, switches, and network cables) for connecting

cloud data centers

204a, 204b, and 204c and facilitating access to cloud computing services by computing systems 206 a-f. In an embodiment, the network represents any combination of one or more local networks, wide area networks, or internetworks coupled by wired or wireless links deployed using terrestrial or satellite connections. Data exchanged over a network is transmitted using a variety of network layer protocols, such as Internet Protocol (IP), multi-protocol label switching (MPLS), Asynchronous Transfer Mode (ATM), Frame Relay (Frame Relay), etc. Further, in embodiments where the network represents a combination of multiple sub-networks, a different network layer protocol is used on each underlying sub-network. In some embodiments, the network represents one or more interconnected internet networks (such as the public internet, etc.).

Computing systems 206a-f or cloud computing service consumers are connected to cloud 202 through network links and network adapters. In embodiments, computing systems 206a-f are implemented as a variety of computing devices, such as servers, desktops, laptops, tablets, smartphones, internet of things (IoT) devices, autonomous vehicles (including cars, drones, space shuttles, trains, buses, and the like), and consumer electronics. In embodiments, computing systems 206a-f are implemented in or as part of other systems.

Fig. 3 illustrates a computer system 300. In an implementation, the computer system 300 is a special purpose computing device. Special purpose computing devices are hardwired to perform the techniques, or include digital electronic devices such as one or more Application Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques according to program instructions in firmware, memory, other storage, or a combination. Such dedicated computing devices may also incorporate custom hardwired logic, ASICs or FPGAs with custom programming to accomplish these techniques. In various embodiments, the special purpose computing device is a desktop computer system, portable computer system, handheld device, network device, or any other apparatus that includes hard wiring and/or program logic to implement these techniques.

In an embodiment, computer system 300 includes a bus 302 or other communication mechanism for communicating information, and a hardware processor 304 coupled with bus 302 for processing information. The hardware processor 304 is, for example, a general purpose microprocessor. Computer system 300 also includes a main memory 306, such as a Random Access Memory (RAM) or other dynamic storage device, coupled to bus 302 for storing information and instructions to be executed by processor 304. In one implementation, main memory 306 is used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 304. When stored in a non-transitory storage medium accessible to processor 304, these instructions cause computer system 300 to become a special-purpose machine that is customized to perform the operations specified in the instructions.

In an embodiment, computer system 300 further includes a Read Only Memory (ROM)308 or other static storage device coupled to bus 302 for storing static information and instructions for processor 304. A storage device 310, such as a magnetic disk, optical disk, solid state drive, or three-dimensional cross-point memory, is provided and coupled to bus 302 to store information and instructions.

In an embodiment, computer system 300 is coupled via bus 302 to a display 312, such as a Cathode Ray Tube (CRT), Liquid Crystal Display (LCD), plasma display, Light Emitting Diode (LED) display, or Organic Light Emitting Diode (OLED) display for displaying information to a computer user. An input device 314, including alphanumeric and other keys, is coupled to bus 302 for communicating information and command selections to processor 304. Another type of user input device is cursor control 316, such as a mouse, a trackball, touch display, or cursor direction keys for communicating direction information and command selections to processor 304 and for controlling cursor movement on display 312. Such input devices typically have two degrees of freedom in two axes, a first axis (e.g., the x-axis) and a second axis (e.g., the y-axis), that allow the device to specify positions in a plane.

According to one embodiment, the techniques herein are performed by computer system 300 in response to processor 304 executing one or more sequences of one or more instructions contained in main memory 306. Such instructions are read into main memory 306 from another storage medium, such as storage device 310. Execution of the sequences of instructions contained in main memory 306 causes processor 304 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.

The term "storage medium" as used herein refers to any non-transitory medium that stores data and/or instructions that cause a machine to operate in a specific manner. Such storage media includes non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, solid-state drives, or three-dimensional cross-point memories, such as storage device 310. Volatile media includes dynamic memory, such as main memory 306. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with a hole pattern, a RAM, a PROM, and EPROM, a FLASH-EPROM, an NV-RAM, or any other memory chip or cartridge.

Storage media is distinct from but may be used in combination with transmission media. Transmission media participate in the transfer of information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 302. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

In an embodiment, various forms of media are involved in carrying one or more sequences of one or more instructions to processor 304 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer loads the instructions into its dynamic memory and sends the instructions over a telephone line using a modem. A modem local to computer system 300 receives the data on the telephone line and uses an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector receives the data carried in the infra-red signal and appropriate circuitry places the data on bus 302. Bus 302 carries the data to main memory 306, from which main memory 306 processor 304 retrieves and executes the instructions. The instructions received by main memory 306 may optionally be stored on storage device 310 either before or after execution by processor 304.

Computer system 300 also includes a communication interface 318 coupled to bus 302. Communication interface 318 provides a two-way data communication coupling to a network link 320 that is connected to a local network 322. For example, communication interface 318 is an Integrated Services Digital Network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 318 is a Local Area Network (LAN) card to provide a data communication connection to a compatible LAN. In some implementations, a wireless link is also implemented. In any such implementation, communication interface 318 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

Network link 320 typically provides data communication through one or more networks to other data devices. For example, network link 320 provides a connection through local network 322 to a host computer 324 or to a cloud data center or equipment operated by an Internet Service Provider (ISP) 326. ISP 326 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the "internet" 328. Local network 322 and internet 328 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 320 and through communication interface 318, which carry the digital data to and from computer system 300, are exemplary forms of transmission media. In an embodiment, network 320 comprises cloud 202 or a portion of cloud 202 as described above.

Computer system 300 sends messages and receives data, including program code, through the network(s), network link 320 and communication interface 318. In an embodiment, computer system 300 receives code for processing. The received code may be executed by processor 304 as it is received, and/or stored in storage device 310, or other non-volatile storage for later execution.

Autonomous vehicle architecture

Fig. 4 illustrates an example architecture 400 for an autonomous vehicle (e.g., AV100 shown in fig. 1). Architecture 400 includes a perception module 402 (sometimes referred to as a perception circuit), a planning module 404 (sometimes referred to as a planning circuit), a control module 406 (sometimes referred to as a control circuit), a positioning module 408 (sometimes referred to as a positioning circuit), and a database module 410 (sometimes referred to as a database circuit). Each module plays a role in the operation of the AV 100. Collectively, the

modules

402, 404, 406, 408, and 410 may be part of the AV system 120 shown in fig. 1. In some embodiments, any of the

modules

402, 404, 406, 408, and 410 are a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application specific integrated circuits [ ASICs ], hardware memory devices, other types of integrated circuits, other types of computer hardware, or a combination of any or all of these).

In use, the planning module 404 receives data representing the destination 412 and determines data representing a trajectory 414 (sometimes referred to as a route) that the AV100 can travel in order to reach (e.g., arrive at) the destination 412. In order for planning module 404 to determine data representing trajectory 414, planning module 404 receives data from perception module 402, positioning module 408, and database module 410.

The perception module 402 identifies nearby physical objects using, for example, one or more sensors 121 as also shown in fig. 1. The objects are classified (e.g., grouped into types such as pedestrian, bicycle, automobile, traffic sign, etc.), and a scene description including the classified objects 416 is provided to the planning module 404.

The planning module 404 also receives data representing the AV location 418 from the positioning module 408. The positioning module 408 determines the AV location by using data from the sensors 121 and data (e.g., geographic data) from the database module 410 to calculate the location. For example, the positioning module 408 uses data from GNSS (global navigation satellite system) sensors and geographic data to calculate the longitude and latitude of the AV. In an embodiment, the data used by the positioning module 408 includes high precision maps with lane geometry attributes, maps describing road network connection attributes, maps describing lane physics attributes such as traffic rate, traffic volume, number of vehicle and bicycle lanes, lane width, lane traffic direction, or lane marker types and locations, or combinations thereof, and maps describing spatial locations of road features such as intersections, traffic signs, or other travel signals of various types, and the like.

The control module 406 receives data representing the track 414 and data representing the AV location 418 and operates the control functions 420 a-420 c of the AV (e.g., steering, throttle, brake, ignition) in a manner that will cause the AV100 to travel the track 414 to the destination 412. For example, if the trajectory 414 includes a left turn, the control module 406 will operate the control functions 420 a-420 c as follows: the steering angle of the steering function will cause the AV100 to turn left and the throttle and brakes will cause the AV100 to pause and wait for a passing pedestrian or vehicle before making a turn.

Autonomous vehicle input

FIG. 5 illustrates examples of inputs 502a-502d (e.g., sensors 121 shown in FIG. 1) and outputs 504a-504d (e.g., sensor data) used by the perception module 402 (FIG. 4). One input 502a is a LiDAR (light detection and ranging) system (e.g., LiDAR 123 shown in FIG. 1). LiDAR is a technology that uses light (e.g., a line of light such as infrared light) to obtain data related to a physical object in its line of sight. The LiDAR system generates LiDAR data as output 504 a. For example, LiDAR data is a collection of 3D or 2D points (also referred to as point clouds) used to construct a representation of the environment 190.

The other input 502b is a RADAR system. RADAR is a technology that uses radio waves to obtain data about nearby physical objects. RADAR may obtain data related to objects that are not within a line of sight of the LiDAR system. The RADAR system 502b generates RADAR data as output 504 b. For example, RADAR data is one or more radio frequency electromagnetic signals used to construct a representation of the environment 190.

Another input 502c is a camera system. Camera systems use one or more cameras (e.g., digital cameras using light sensors such as charge coupled devices CCD) to acquire information about nearby physical objects. The camera system generates camera data as output 504 c. The camera data is generally in the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, or the like). In some examples, the camera system has multiple independent cameras, for example for the purpose of stereoscopic imagery (stereo vision), which enables the camera system to perceive depth. Although the object perceived by the camera system is described herein as "nearby," this is with respect to AV. In use, the camera system may be configured to "see" objects that are far away (e.g., as far as 1 km or more in front of the AV). Accordingly, the camera system may have features such as a sensor and a lens optimized for sensing a distant object.

Another input 502d is a Traffic Light Detection (TLD) system. TLD systems use one or more cameras to obtain information about traffic lights, street signs, and other physical objects that provide visual navigation information. The TLD system generates TLD data as output 504 d. The TLD data often takes the form of image data (e.g., data in an image data format such as RAW, JPEG, PNG, etc.). The TLD system differs from the system containing the camera in that: TLD systems use cameras with a wide field of view (e.g., using a wide-angle lens or a fisheye lens) to obtain information about as many physical objects as possible that provide visual navigation information, so that the AV100 can access all relevant navigation information provided by these objects. For example, the viewing angle of a TLD system may be about 120 degrees or greater.

In some embodiments, the outputs 504a-504d are combined using sensor fusion techniques. Thus, the individual outputs 504a-504d are provided to other systems of the AV100 (e.g., to the planning module 404 as shown in fig. 4), or the combined outputs may be provided to other systems in the form of a single combined output or multiple combined outputs of the same type (e.g., using the same combining technique or combining the same output or both) or different types of single combined output or multiple combined outputs (e.g., using different individual combining techniques or combining different individual outputs or both). In some embodiments, early fusion techniques are used. Early fusion techniques were characterized by: the outputs are combined before one or more data processing steps are applied to the combined output. In some embodiments, post-fusion techniques are used. The later stage fusion technology is characterized in that: after applying one or more data processing steps to the individual outputs, the outputs are combined.

FIG. 6 illustrates an example of a LiDAR system 602 (e.g., input 502a shown in FIG. 5). The LiDAR system 602 emits light 604a-604c from a light emitter 606 (e.g., a laser emitter). Light emitted by LiDAR systems is typically not in the visible spectrum; for example, infrared light is often used. Some of the emitted light 604b encounters a physical object 608 (e.g., a vehicle) and is reflected back to the LiDAR system 602. (light emitted from a LiDAR system does not typically penetrate physical objects, e.g., solid form physical objects.) the LiDAR system 602 also has one or more light detectors 610 for detecting reflected light. In an embodiment, one or more data processing systems associated with a LiDAR system generate an image 612 that represents a field of view 614 of the LiDAR system. The image 612 includes information representing the boundary 616 of the physical object 608. Thus, the image 612 is used to determine the boundaries 616 of one or more physical objects in the vicinity of the AV.

FIG. 7 shows the LiDAR system 602 in operation. In the scenario shown in this figure, the AV100 receives both camera system output 504c in the form of images 702 and LiDAR system output 504a in the form of LiDAR data points 704. In use, the data processing system of the AV100 compares the image 702 with the data points 704. In particular, a physical object 706 identified in the image 702 is also identified in the data points 704. In this way, the AV100 perceives the boundaries of the physical object based on the contours and densities of the data points 704.

FIG. 8 shows additional details of the operation of a LiDAR system 602. As described above, the AV100 detects boundaries of physical objects based on characteristics of data points detected by the LiDAR system 602. As shown in FIG. 8, a flat object, such as the ground 802, will reflect the light 804a-804d emitted from the LiDAR system 602 in a consistent manner. In other words, because the LiDAR system 602 emits light using consistent intervals, the ground 802 will reflect light back to the LiDAR system 602 at the same consistent intervals. As the AV100 travels on the ground 802, the LiDAR system 602 will continue to detect light reflected by the next valid waypoint 806 without blocking the road east and west. However, if the object 808 blocks the road, the light 804e-804f emitted by the LiDAR system 602 will reflect from the points 810a-810b in a manner that is inconsistent with the expected consistency. From this information, the AV100 can determine that the object 808 is present.

Path planning

Fig. 9 illustrates a block diagram 900 of the relationship between the inputs and outputs of planning module 404 (e.g., as illustrated in fig. 4). Generally, the output of the planning module 404 is a route 902 from a starting point 904 (e.g., a source location or an initial location) to an ending point 906 (e.g., a destination or a final location). Route 902 is typically defined by one or more road segments. For example, a road segment refers to a distance to be traveled on at least a portion of a street, road, highway, driveway, or other physical area suitable for a car to travel. In some examples, if AV100 is an off-road capable vehicle, such as a four-wheel drive (4WD) or all-wheel drive (AWD) car, SUV, or pick-up, for example, route 902 includes "off-road" road segments, such as unpaved paths or open fields.

In addition to the route 902, the planning module outputs lane-level route planning data 908. The lane-level routing data 908 is used to travel through segments of the route 902 at particular times based on the conditions of the segments. For example, if the route 902 includes a multi-lane highway, the lane-level routing data 908 includes trajectory planning data 910, where the AV100 can use the trajectory planning data 910 to select a lane from among the multiple lanes, e.g., based on whether an exit is adjacent, whether there are other vehicles in one or more of the multiple lanes, or other factors that change over the course of several minutes or less. Similarly, in some implementations, the lane-level routing data 908 includes rate constraints 912 that are specific to a section of the route 902. For example, if the road segment includes pedestrians or unexpected traffic, the rate constraint 912 may limit the AV100 to a slower than expected rate of travel, such as a rate based on the speed limit data for the road segment.

In an embodiment, inputs to planning module 404 include database data 914 (e.g., from database module 410 shown in fig. 4), current location data 916 (e.g., AV location 418 shown in fig. 4), destination data 918 (e.g., for destination 412 shown in fig. 4), and object data 920 (e.g., classified object 416 as perceived by perception module 402 shown in fig. 4). In some embodiments, database data 914 includes rules used in planning. The rules are specified using a formal language (e.g., using boolean logic). In any given situation encountered by the AV100, at least some of these rules will apply to that situation. A rule is applicable to a given situation if the rule has a condition satisfied based on information available to the AV100 (e.g., information related to the surrounding environment). The rules may have priority. For example, the rule of "move to the leftmost lane if the highway is an expressway" may have a lower priority than "move to the rightmost lane if the exit is close within one mile".

Fig. 10 illustrates a directed graph 1000 used in path planning (e.g., by planning module 404 (fig. 4)). In general, a directed graph 1000, such as the directed graph shown in FIG. 10, is used to determine a path between any starting point 1002 and ending point 1004. In the real world, the distance separating the start 1002 and end 1004 may be relatively large (e.g., in two different metropolitan areas), or may be relatively small (e.g., two intersections adjacent a city block or two lanes of a multi-lane road).

In an embodiment, directed graph 1000 has nodes 1006a-1006d representing different places AV100 may occupy between a start point 1002 and an end point 1004. In some examples, nodes 1006a-1006d represent segments of a road, for example, where the start point 1002 and the end point 1004 represent different metropolitan areas. In some examples, for example, where the start point 1002 and the end point 1004 represent different locations on the same road, the nodes 1006a-1006d represent different locations on the road. Thus, the directed graph 1000 includes information at different levels of granularity. In an embodiment, a directed graph with high granularity is also a subgraph of another directed graph with a larger scale. For example, most information of a directed graph with a starting point 1002 and an ending point 1004 that are far away (e.g., many miles away) is at a low granularity, and the directed graph is based on stored data, but the directed graph also includes some high granularity information for a portion of the directed graph that represents a physical location in the field of view of the AV 100.

Nodes 1006a-1006d are distinct from objects 1008a-1008b that cannot overlap with the nodes. In an embodiment, at low granularity, objects 1008a-1008b represent areas that the car cannot pass through, such as areas without streets or roads. At high granularity, objects 1008a-1008b represent physical objects in the field of view of AV100, such as other cars, pedestrians, or other entities with which AV100 cannot share a physical space. In embodiments, some or all of the objects 1008a-1008b are static objects (e.g., objects that do not change location, such as street lights or utility poles, etc.) or dynamic objects (e.g., objects that are capable of changing location, such as pedestrians or other cars, etc.).

Nodes 1006a-1006d are connected by edges 1010a-1010 c. If two nodes 1006a-1006b are connected by an edge 1010a, the AV100 may travel between one node 1006a and the other node 1006b, e.g., without having to travel to an intermediate node before reaching the other node 1006 b. (when referring to AV100 traveling between nodes, meaning that AV100 travels between two physical locations represented by respective nodes.) edges 1010a-1010c are generally bi-directional in the sense that AV100 travels from a first node to a second node, or from a second node to a first node. In an embodiment, edges 1010a-1010c are unidirectional in the sense that AV100 may travel from a first node to a second node, whereas AV100 may not travel from the second node to the first node. The edges 1010a-1010c are unidirectional where the edges 1010a-1010c represent individual lanes of, for example, a unidirectional street, road, or highway, or other feature that can only be traversed in one direction due to legal or physical constraints.

In an embodiment, planning module 404 uses directed graph 1000 to identify a path 1012 made up of nodes and edges between start point 1002 and end point 1004.

Edges 1010a-1010c have associated costs 1014a-1014 b. The costs 1014a-1014b are values representing the resources that would be spent if the AV100 selected the edge. A typical resource is time. For example, if one edge 1010a represents twice the physical distance as represented by the other edge 1010b, the associated cost 1014a of the first edge 1010a may be twice the associated cost 1014b of the second edge 1010 b. Other factors that affect time include expected traffic, number of intersections, speed limits, etc. Another typical resource is fuel economy. The two edges 1010a-1010b may represent the same physical distance, but one edge 1010a may require more fuel than the other edge 1010b, e.g., due to road conditions, expected weather, etc.

When the planning module 404 identifies a path 1012 between the start point 1002 and the end point 1004, the planning module 404 typically selects a path that is optimized for cost, e.g., a path having a minimum total cost when adding the individual costs of the edges together.

Autonomous vehicle control

Fig. 11 illustrates a block diagram 1100 of inputs and outputs of the control module 406 (e.g., as shown in fig. 4). The control module operates in accordance with a controller 1102, the controller 1102 including, for example: one or more processors (e.g., one or more computer processors such as a microprocessor or microcontroller, or both) similar to processor 304; short-term and/or long-term data storage devices (e.g., memory, random access memory or flash memory or both) similar to main memory 306, ROM 308, and storage device 310; and instructions stored in the memory that, when executed (e.g., by one or more processors), perform the operations of the controller 1102.

In an embodiment, the controller 1102 receives data representing a desired output 1104. The desired output 1104 generally includes speed, such as speed and heading. The desired output 1104 may be based on, for example, data received from the planning module 404 (e.g., as shown in fig. 4). Depending on the desired output 1104, the controller 1102 generates data that can be used as a throttle input 1106 and a steering input 1108. The throttle input 1106 represents the magnitude of a throttle (e.g., acceleration control) that engages the AV100 to achieve the desired output 1104, such as by engaging a steering pedal or engaging another throttle control. In some examples, the throttle input 1106 also includes data that can be used to engage a brake (e.g., deceleration control) of the AV 100. Steering input 1108 represents a steering angle, such as an angle at which steering control of the AV (e.g., a steering wheel, a steering angle actuator, or other function for controlling the steering angle) should be positioned to achieve the desired output 1104.

In an embodiment, the controller 1102 receives feedback for use in adjusting the inputs provided to the throttle and steering. For example, if the AV100 encounters a disturbance 1110, such as a hill, the measured rate 1112 of the AV100 drops below the desired output rate. In an embodiment, any measured output 1114 is provided to the controller 1102 such that the required adjustments are made, for example, based on the difference 1113 between the measured rate and the desired output. The measurement outputs 1114 include a measurement location 1116, a measurement speed 1118 (including speed and heading), a measurement acceleration 1120, and other outputs measurable by sensors of the AV 100.

In an embodiment, information related to the disturbance 1110 is detected in advance, for example, by a sensor such as a camera or LiDAR sensor, and provided to the predictive feedback module 1122. The predictive feedback module 1122 then provides information to the controller 1102 that the controller 1102 can use to adjust accordingly. For example, if a sensor of the AV100 detects ("sees") a hill, the controller 1102 may use this information to prepare to engage the throttle at the appropriate time to avoid significant deceleration.

Fig. 12 shows a block diagram 1200 of the inputs, outputs, and components of a controller 1102. The controller 1102 has a rate analyzer 1202 that affects the operation of a throttle/brake controller 1204. For example, the rate analyzer 1202 instructs the throttle/brake controller 1204 to accelerate or decelerate using the throttle/brake 1206 based on feedback received by the controller 1102 and processed by the rate analyzer 1202, for example.

The controller 1102 also has a lateral tracking controller 1208 that affects the operation of the steering wheel controller 1210. For example, the lateral tracking controller 1208 instructs the steering wheel controller 1210 to adjust the position of the steering angle actuator 1212, based on feedback received by the controller 1102 and processed by the lateral tracking controller 1208, for example.

The controller 1102 receives several inputs for determining how to control the throttle/brake 1206 and the steering angle actuator 1212. The planning module 404 provides information used by the controller 1102 to, for example, select a heading at which the AV100 is to begin operation and determine which road segment to traverse when the AV100 reaches an intersection. The positioning module 408 provides information describing the current location of the AV100 to the controller 1102, for example, so that the controller 1102 can determine whether the AV100 is in a location that is expected based on the manner in which the throttle/brake 1206 and steering angle actuator 1212 are being controlled. In an embodiment, the controller 1102 receives information from other inputs 1214, such as information received from a database, a computer network, or the like.

Computer system for object detection

Fig. 13A illustrates an exemplary sensor signal 1300 (e.g., a RADAR signal). In particular, fig. 13A illustrates the magnitude (vertical axis) of the sensor signal 1300 over time (horizontal axis). Signal 1300 includes

transmission pulses

1302A, 1302B, and 1302C. In some embodiments, signal 1300 includes one, two, or more than three transmit pulses. In some embodiments, signal 1300 is referred to as a scan signal. The

transmission pulses

1302A, 1302B, and 1302C each have a duration (pulse width) of time τ. In some embodiments, the durations of the transmission pulses in signal 1300 are not all the same. Signal 1300 has a pulse repetition period (pulse repetition time) of the PRT defined by the time from the start of one transmit pulse to the start of the next transmit pulse. The inverse of the pulse repetition period is the pulse repetition frequency.

Signal 1300 includes received signals 1304A, 1304B, and 1304C, corresponding to signals received immediately after transmission of

transmission pulses

1302A, 1302B, and 1302C, respectively. For example, the received signal 1304A is a signal received as a result of the transmitted signal 1302A reflecting off of an object in the sensor's surroundings toward a receiver. In FIG. 13A, time t after transmission of

transmission pulses

1302A, 1302B, and 1302C_rxThe signal is internally received.

Peaks

1306A, 1306B, and 1306C represent portions of the

transmission pulses

1302A, 1302B, and 1302C, respectively, that are reflected by objects that are relatively strongly reflecting. The portions of received

signals

1304A, 1304B, and 1304C before and after

peaks

1306A, 1306B, and 1306C may be non-zero due to noise, such as noise in the receiver, in-band signals from the environment that are not a result of reflections of the transmitted pulses, or reflections of the transmitted pulses from atmospheric elements in the environment.

The distance of the

object causing peaks

1306A, 1306B and 1306C is determined based on the time t between the peaks and the corresponding transmitted pulses, which is the time it takes for the transmitted signal to propagate to the object and then back to the receiver. The distance of the object is R ═ t × c/2.

Fig. 13B illustrates a larger scale depiction of the transmit pulse 1302A. In particular, fig. 13B depicts a transmitter signal or carrier signal in the transmit pulse 1302A. The transmitter frequency or carrier frequency is 1/λ, where λ is the wavelength of the transmitter signal. In some embodiments, all transmission pulses in signal 1300 have the same transmitter frequency. In some embodiments, signal 1300 includes transmission pulses having different transmitter frequencies.

Some sensors use the doppler effect, where the frequency of the signal reflected from a moving object is different (or shifted) compared to the frequency of the signal incident on the moving object. Doppler frequency is given by

Is shown in which f_DIs the Doppler frequency, v_rIs the radial velocity of the object, c₀Is the speed of light, and f_txIs the transmitter frequency. The doppler frequency of the object is measured by detecting the phase of the received signal. For a pulsed signal, such as signal 1300, the phase of the received signal is measured (or sampled) at the pulse repetition frequency. According to the Nyquist sampling theorem, f_sMay be captured from a sample having a sampling rate at-f _s2 to f_sAll information of the signal for frequencies within the frequency window between/2. The window has f_sThe total width of (a). Frequencies outside the window will produce aliasing with frequencies inside the window. Frequencies outside the window are considered to be aliased or folded into the window.

For systems where the detection signal may have frequencies outside the window, the detection frequency is ambiguous. That is, the detection frequency will appear within the window, but may actually be caused by frequencies outside the window that have been folded into the window.

Radial velocity (interchangeably referred to as range rate) corresponding to the Doppler frequency is

Since the sampling frequency for signal 1300 is PRF, the true radial velocity v_rIs collapsed by the Doppler coverage vun (also referred to as range-rate window size) where

Thus, the radial velocity v is detected_det＝v_r±N*vun。

In general, the Doppler coverage window (also referred to as range-rate window) w_DAt an initial radial velocity v₀Start and extend the doppler coverage vun such that w_D＝(v₀,v₀+ vun). The doppler coverage is referred to herein as the range-rate window size. In some embodiments of the present invention, the,

(e.g., the range-rate window is centered at 0m/s range-rate).

Fig. 14A illustrates an embodiment with a RADAR system 502b installed on AV 100. The physical object 608 is at a velocity v_r(velocity v)_rSince physical object 608 is approaching AV100 and is taking a negative value, the distance between physical object 608 and AV100 is reduced over time). v. of_rRepresenting the relative range-rate between the AV100 and the physical object 608.

FIG. 14B illustrates range-rate windows for a first scan by the RADAR system 502B

Based on the characteristics of the first scan (e.g., PRF and carrier frequency), the first scan provides doppler coverage vun₁. In general, the range-rate window is defined by the relationship:

where i is the window index and j is the scan index.

The true range-rate v of the physical object 608_rIndicated by the arrows shown in solid lines. Obviously, the true range rate v_rIn the main Doppler window

And (c) out. In the embodiment illustrated in FIG. 14B, the true range-rate v_rFall within the Doppler window

Within. Thus, when the RADAR system 502b detects the physical object 608, the true range-rate of the physical object 608v_rIs folded to the main Doppler window

Such that the physical object 608 is detected as having range-rates

In general, having a velocity v_rWill have a detection range rate v_det＝v_r-n. vun, where n is the true range rate v_rThe index of the falling doppler window.

FIG. 14C illustrates range-rate windows for a second scan by the RADAR system 502b

Based on the characteristics of the second scan (e.g., PRF and carrier frequency), the second scan provides doppler coverage vun₂. In the embodiment illustrated in FIG. 14C, the Doppler coverage of the second scan is greater than the Doppler coverage of the first scan, as by a Doppler window

Indicated by the width of (a).

And (c) out. In the embodiment illustrated in FIG. 14C, the true range-rate v_rFall within the Doppler window

Within. Thus, when the physical object 608 is detected by the RADAR system 502b, the true range-rate v of the physical object 608_rIs folded to the main Doppler window

In such a way that the physical object 608 is detectedMeasured as having range-change rates

Notably, since the doppler coverage of the second scan is different from the doppler coverage of the first scan, the detected range rate of the second scan is different from the detected range rate of the first scan even though the true range rate for the two scans of the physical object 608 is the same. This difference is a potential problem. For example, if the difference in detection range rates is not properly accounted for, the detection results of physical object 608 in the second scan may be identified as detection results of a different object or not properly associated with the detection results of physical object 608 in the first scan (e.g., for tracking purposes).

The following techniques may be used to expand the detected range-rates of the objects to determine the true range-rates of the detected objects.

The true range-rate can be expressed as

Wherein v is_rIs the true radial velocity of the beam of radiation,

is the detected velocity at the time indicated by the index k (the index k may also correspond to the scan), N_kIs the index of the window into which the true range-rate falls at the time represented by index k, and vun_kIs the doppler coverage at the time represented by the index k.

The true range-rate of the test object can be estimated by solving an acceptable solution to the following equation:

(1)

wherein

And

is the detection range rate, N, of the first and second scans (respectively)₁And N₂(respectively) an index of the window into which the true range-rate for the first scan and the second scan falls, and vun₁And vun₂Is the doppler coverage for the first and second scans (respectively).

For N₁Solving equation (1) provides the following:

(2)

wherein N is_diff＝N₂-N₁。

For N_diffSolves equation (2) to obtain N_{1_calc}(e.g., based on N)_diffIs selected value of N₁Calculated value of). For two consecutive scans, it is possible that the true range rate will fall within the window with the same index for both scans (e.g., N₁Is-1 and N₂1) or fall within a window with immediately adjacent indices (e.g., N)₁0 and N₂-1); in the first case, N_diff0, and in the second case, N_diff＝-1。

Evaluating N against specified criteria₁The calculated value of (a). In some embodiments, the criteria includes if N₁Is less than (or less than or equal to) a threshold value (e.g., 2, 3, or 4). E.g. N having an absolute value above a threshold₁May correspond to range rates that are unlikely to be correct for a particular environment. In an AV environment, for example, the threshold may be set to correspond to a maximum expected velocity of other vehicles or objects that the AV is quite likely to encounter. N above threshold₁May correspond to range rates that are not practical in such situations (e.g., range rates that exceed the maximum possible relative velocity between the two vehicles).

In some embodiments, the criteria includes if N₁Of a calculated value of (A) in an integer valueWithin a threshold amount. As described above, N₁An index representing the window into which the true range-rate falls. In some embodiments, the index of the window is an integer value, and thus is relatively not close to N of the integer value₁Is meaningless and indicates that it is used to calculate N₁N of value (d)_diffIs incorrect (and if ultimately used as described below, will result in incorrect estimates of the true range rates). In some embodiments, this criterion is met if both conditions described above are met.

If N is present₁If the calculated value of (A) does not satisfy the specified criterion, then N is specified_diffTo obtain N by solving equation (2) for different values of₁Another calculated value of (a). In some embodiments, for N_diffUntil N is obtained that satisfies the specified criteria, equation (2) is solved for the different values of (c)₁Until the calculated value of (2). In some embodiments, for N_diffUntil N is obtained that satisfies the specified criteria, equation (2) is solved for the different values of (c)₁Until the calculated value for N has been used or until the calculated value for N has been used_diffThe defined set of values of (e.g., 0, -1, 1), whichever occurs first.

If N is present₁If the calculated value of (A) meets the specified criteria, then N is added₁And for calculating N₁N of (A)_diffIs used to calculate N_{2_calc}(e.g., N)₂Calculated value of). Then, N is added₂Is used according to the equation

To determine (e.g., estimate) the true range-rates.

In some embodiments, the equation may be based on

From N₁Directly estimates the range-rate without calculating N_{2_calc}。

An exemplary embodiment of the above described techniques may be represented in pseudo code as follows:

consider, for example, having v₀＝-23m/s、vun₁＝35m/s、vun₂An embodiment of 30m/s, such that all detection results of the first scan are folded into a window (-23m/s, 12m/s), and all detection results of the second scan are folded into a window (-23m/s, 7 m/s).

In FIG. 14A, if AV100 is stationary and physical object 608 is approaching AV100 at a rate of 32m/s, then physical object 608 has a true range rate v outside the main window for the first scan and the second scan_r-32 m/s. In this example, the first scan detects a range rate of

m/s +35m/s is 3m/s, and the range-rate detected by the second scanning is

Notably, the detected range rate of the first scan is different from the detected range rate of the second scan, and the two detected range rates do not reflect the actual range rate of the physical object 608. Selection of N_diffZero and solve equation (2), which provides N of-1_{1_calc}The value is obtained. Since-1 is an integer and has a relatively small absolute value (e.g., below the threshold of 3, based on the detected range rate and Doppler coverage of the first scan, which corresponds to an approaching range rate of 102m/s and a separating range rate of 108 m/s), N_{1_calc}For estimating the true range-rate. Using N₁Is-1 and N_diffProviding N as 0_{2_calc}Is-1. Using N_{2_calc}Is given as-1

Which in this example is the true range-rate of physical object 608.

As another example, if AV100 is stationary and physical object 608 is moving away from AV100 at a rate of 9m/s, then physical object 608 has a true range rate v inside the main window for the first scan and outside the main window for the second scan_r9 m/s. In this example, the range rate detected by the first scan is

And the range-rate detected by the second scan is

Notably, the detected range rate of the first scan is different from the detected range rate of the second scan, and only the detected range rate of the first scan reflects the actual range rate of the physical object 608. Selection of N_diffZero and solve equation (2), which provides N of-6_{1_calc}The value is obtained. Although-6 is an integer, it has a relatively large absolute value (e.g., greater than a predefined threshold). Due to N for 0_diffValue calculated N₁The value does not satisfy the specified criterion, and is therefore for N_diffCalculating N from the difference value of₁Another value of (a). Selection of N_diffProviding N as 0 for 1₁Is an integer and small. Using N₁0 and N_diffProviding N as 1_{2_calc}1. Using N_{2_calc}Is given as 1

Which in this example is the true range-rate of physical object 608.

In some embodiments, the solution to equation (1) above may be solved in other ways. Generally, the technique attempts to solve for constraints that satisfy equation (1) and satisfy a specified criterion (e.g., N)₁And N₂Is the same integer or an adjacent integer) of N₁And N₂A combination of values of (c). Once solved to satisfy equation (1)N constrained and satisfying specified criteria₁And N₂Using N of the combination₁And/or N₂The values are used to estimate the true range-rate of the detected object (e.g., using the detected range-rate, corresponding Doppler coverage, and N from the selected combination₁And/or N₂Values to develop the detection range rate according to equation (2).

Example Process for detecting an object and operating a vehicle based on the detection of the object

Fig. 15 is a flow diagram of an example process 1500 (also referred to as a method) for detecting objects in an environment and operating a vehicle based on the detection of the objects. For convenience, process 1500 will be described as being performed by a system of one or more computers located at one or more sites. For example, the AV system 120 of fig. 1 or the computer system 300 of fig. 3, suitably programmed in accordance with the present description, can perform the process 1500. Process 1500 may be used to implement the techniques described above with reference to fig. 13A-13B and 14A-14C.

At block 1502, a first transmission signal (e.g., signal 1300; transmission pulses 1302A-1302C; a first RADAR signal; a plurality of electromagnetic pulses at a first Pulse Repetition Frequency (PRF)) is transmitted into an environment. The first transmission signal corresponds to a first range rate window size (e.g., vun)₁(ii) a Doppler coverage of the first scan; the first range-rate window size corresponds to the first PRF). In some embodiments, the first transmission signal is transmitted using one or more transmitters (e.g., sensor 121, RADAR transmitter, LiDAR light source, ultrasound sensor, time-of-flight (TOF) depth sensor).

In some embodiments, the range rate window size (e.g., vun) is based on the PRF and the transmitter frequency of the transmission signal (e.g.,

). In some embodiments, the first transmission signal includes a first plurality of pulses having a first transmitter frequency and a first pulse repetition frequency, wherein the first range rate window size corresponds to the first transmitter frequency and the first pulse repetition frequencyThe pulse repetition frequency.

At block 1504, a first received signal (e.g., received signals 1304A-1304C) is received. The first received signal includes at least a portion (e.g., 1306A-1306C) of the first transmitted signal that has been reflected from an object in the environment. In some embodiments, the first receive signal is received using one or more receivers (e.g., sensor 121, a RADAR receiver, or a LiDAR receiver).

At block 1506, a first detection range rate of the object is determined based on the first received signal (e.g.,

). In some embodiments, a first detection range rate of the subject is determined using a processing circuit (e.g., the computer processor 146 or the processor 304).

At block 1508, after transmitting the first transmission signal, a second transmission signal (e.g., signal 1300; transmission pulses 1302A-1302C; a second RADAR scan signal; a plurality of electromagnetic pulses at a second PRF) is transmitted into the environment. The second transmission signal corresponds to a second range rate window size (e.g., vun)₂(ii) a The second range-rate window size corresponds to the second PRF). In some embodiments, the second transmission signal is transmitted using one or more transmitters. In some embodiments, the second transmission signal includes a second plurality of pulses having a second transmitter frequency and a second pulse repetition frequency different from the first pulse repetition frequency, wherein the second range rate window size corresponds to the second transmitter frequency and the second pulse repetition frequency.

In some embodiments, the range rate window size (e.g., doppler coverage) is different for the two transmitted signals. For example, the first range-rate window size is different from the second range-rate window size. Due to the folding, different range-rate window sizes may result in different detection range rates (e.g., if the actual subject range rates are greater than the first range-rate window size and the second range-rate window size, and the first range-rate window size and the second range-rate window size are different, the actual subject range rates will be folded into windows of detectable distance at different range rates for the first received signal than for the second received signal).

In some embodiments, the first transmission signal has a first pulse repetition frequency and the second transmission signal has a second pulse repetition frequency different from the first pulse repetition frequency. In some embodiments, the transmitter frequency for the first transmission signal is the same as the transmitter frequency for the second transmission signal (e.g., the transmitter frequency remains the same between scans). Varying the PRF may vary the range-rate window size. In some embodiments, the first range-rate window size is based on the first pulse repetition frequency, and the second range-rate window size is based on the second pulse repetition frequency and is different from the first range-rate window size.

At block 1510, a second received signal (e.g., received signals 1304A-1304C) is received. The second received signal includes at least a portion (e.g., 1306A-1306C) of the second transmitted signal that has been reflected from an object in the environment. In some embodiments, the second received signal is received using one or more receivers.

The sensor scan signals (e.g., the first and second transmit signals and the first and second receive signals) may be from the same sensor or different sensors.

In some embodiments, transmitting the first transmission signal is performed with a first transmitter of the one or more transmitters and transmitting the second transmission signal is performed with a second transmitter of the one or more transmitters different from the first transmitter. In some embodiments, transmitting the first transmission signal and transmitting the second transmission signal are performed with the same transmitter of the one or more transmitters. In some embodiments, receiving the first received signal is performed with a first receiver of the one or more receivers and receiving the second received signal is performed with a second receiver of the one or more receivers different from the first receiver. In some embodiments, receiving the first received signal and receiving the second received signal are performed with a same receiver of the one or more receivers.

At block 1512, based onThe second received signal determines a second detection range rate of the subject (e.g.,

). In some embodiments, a second detection range rate of the object is determined using the processing circuit.

At block 1514, the difference (e.g., N) is indexed based at least in part on the first range-rate window_diffThe (assumed) difference between the indices of the range-rate windows for which there are real range-rates in the second signal and the indices of the range-rate windows for which there are real range-rates in the first signal) to compute a first range-rate window index (e.g., N)₁). In some embodiments, the first range-rate window index difference is an integer (e.g., 1, 0, 1). In some embodiments, the first range-rate window index is calculated based at least in part on the first range-rate window index difference and one or more of: the first rate of detection range (e.g.,

) The second detection range-rate (e.g.,

) A first range-rate window size (e.g., vun)₁) A second range-rate window size (e.g., vun)₂) And combinations thereof. In some embodiments, the first range-rate window index is calculated using a processing circuit.

In some embodiments, according to the equation

To calculate a first range-rate window index, where N₁Is the index of the first range-rate window,

is the first rate of detection of the range-rate,

is the second detection range rate, vun₁Is the first range-rate window size, vun₂Is the second range-rate window size, and N_diffIs the first range-rate window index difference.

The first range-rate window index may be calculated conditional on the first received signal and the second received signal satisfying a specified criterion (e.g., a gating criterion). In some embodiments, calculating the first range-rate window index is performed in accordance with a determination that a gating criterion is satisfied, wherein the gating criterion is based on the first received signal and the second received signal. The gating criteria may be based on the sensor coordinates (distance and angle) or the global coordinates (euclidean location) of the detected object. For example, the detected distance and/or angle of the object in the first and second received signals must be within a threshold distance and/or angle of each other. In some embodiments, the range-rate expansion operations described herein are performed on all associated pairs of detection results (e.g., detection results from first and second received signals that satisfy the gating criteria). In some embodiments, the expansion is performed only on the pair of detection results that results in the least cost (e.g., the pair of detection results that have the closest spatial relationship).

Since the detection range rates may be folded (and thus erroneous) and result in lost correlations (e.g., the detection results that should be correlated if there is no difference in detection range rates), the detection range rates may be ignored when evaluating the gating criteria. In some embodiments, the gating criteria is not based on the first range rate or the second range rate.

At block 1516, a determination is made as to whether the first range-rate window index meets a predefined criterion (e.g., whether the index is within a range of predefined integer values (such as, -1, 0, or 1)).

At block 1518, in accordance with a determination that the first range-rate window index satisfies the predefined criteria, estimated range-rates are calculated based at least in part on the first range-rate window index differences. In some embodiments, the estimated range-rates are calculated using processing circuitry.

In some embodiments, according to the equation

To calculate the estimated range-rate, where N₂＝N₁+N_diff，v_{r_est}Is the estimation of the range-rate,

is the second detection range rate, N₁Is the first range-rate window index, N_diffIs the first range-rate window index difference, and vun₂Is the second range-rate window size. In some embodiments, the techniques (e.g., algorithm (s)) assume that the range rate of the detected object does not change significantly (e.g., is constant or approximately constant) from the first received signal to the second received signal (e.g., the object does not have a significant acceleration in distance or change in range rate between scans).

In accordance with a determination that the first range-rate window index does not satisfy the predefined criteria, the calculation of the estimated range-rates based on the first range-rate window index differences is abandoned (e.g., the system does not calculate the estimated range-rates based on the first range-rate window index differences, and returns to block 1514 to calculate new range-rate window indices based on the different range-rate window index differences).

In some embodiments, in accordance with a determination that the first range-rate window index does not satisfy the predefined criteria, a second range-rate window index is computed for different range-rate window index differences to try and solve for the range-rate window index that satisfies the predefined criteria. For example, based on a second range-rate window index difference (e.g., a different range-rate window index difference; N) in accordance with a determination that the first range-rate window index does not satisfy the predefined criteria_diffDifferent values of) to compute a second range-rate window index. In some embodiments, the second range-rate window index is calculated based on the second range-rate window index difference and one or more of: a first detection range rate, a second detection range rate, a first range rate window size, a second range rate window size, and combinations thereof. In some embodiments, the second distance variation is calculated using processing circuitryA rate window index. In accordance with a determination that the second range-rate window index satisfies a predefined criterion (e.g., the second range-rate window index is within a predefined range of integer values (such as-1, 0, or 1)), the estimated range-rates are calculated based on the second range-rate window index differences. In some embodiments, the second range-rate window index and/or the estimated range-rate are calculated using processing circuitry.

One or more of the operations described with reference to

blocks

1514, 1516, and 1518 (e.g., range-rate expansion; calculating range-rate window indices and estimated range rates) may be performed outside of a conventional tracking algorithm (e.g., kalman filter) or tracking module, and then the detection result(s) and expanded range rate(s) (e.g., estimated range rate (s)) are provided to the tracker. In some embodiments, the estimated range-rates are transmitted to the tracking circuit. In some embodiments, the estimated range-rates are transmitted to the tracking circuit using the processing circuit.

The detection results may be associated with each other if the expansion range rates of the detection results match. In some embodiments, the detection result of the second received signal is associated with the detection result of the third received signal in dependence on determining that the estimated range rate (e.g. of the detection results of the first received signal and/or the second received signal) and the estimated range rate of the third received signal satisfy a matching criterion.

A distance check may be performed to determine whether to associate the detection results with each other. The distance check may include predicting a current distance based on previous distances and values of the range rate. For example, it can be based on the equation

To determine a predicted distance, wherein range₁Is an estimated (e.g., detected or filtered) distance of an object from a first received signal (e.g., a first scan), a cycleTime is a time between the first received signal and a second received signal (e.g., a time between scans), and

is from the firstAn estimated range-rate of a signal.

If the predicted distance does not match the current detected distance, the detection results are not correlated. For example, if | ranging predicted₂-range₂If the relation of | > delta Range threshold is established, the detection results are not related, wherein range₂Is an estimated (e.g., detected) distance of the object based on the second received signal, and deltarangethreshold is a maximum allowed difference between the predicted distance and the detected distance.

In some embodiments, the estimated range rates are second estimated range rates, and the predicted range is determined based on a first detected range based on the first received signal (e.g., and not the second received signal) and the first estimated range rates. A second detected distance is determined based on the second received signal and, in accordance with a determination that a difference between the predicted distance and the second detected distance exceeds a distance threshold, no association of the second received signal with the first received signal is performed.

The detection results for which the detection range rate has been expanded can be flagged. In some embodiments, the estimated range-rates are indicated as being based on non-zero range-rate window index differences in accordance with a determination that the first range-rate window index satisfies a predefined criterion and that the first range-rate window index difference is not zero.

The estimated range-rate calculated at block 1518 may be used to control the autonomous vehicle. In some embodiments, the autonomous vehicle is controlled using control circuitry.

In the previous description, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. Additionally, when the term "further comprising" is used in the preceding description or the appended claims, the following of the phrase may be additional steps or entities, or sub-steps/sub-entities of previously described steps or entities.

The following describes embodiments of the present disclosure:

1. a method, comprising:

transmitting, using one or more transmitters, a first transmission signal into an environment, the first transmission signal corresponding to a first range-rate window size;

receiving, using one or more receivers, a first received signal comprising at least a portion of the first transmitted signal that has been reflected from an object in the environment;

determining, using a processing circuit, a first detection range rate of the object based on the first received signal;

transmitting, using the one or more transmitters, a second transmission signal into the environment after transmitting the first transmission signal, the second transmission signal corresponding to a second range rate window size;

receiving, using the one or more receivers, a second received signal comprising at least a portion of the second transmitted signal that has been reflected from the object in the environment;

determining, using the processing circuit, a second detection range rate of the object based on the second received signal;

calculating, using the processing circuit, a first range-rate window index based on a first range-rate window index difference, the first detection range rate, the second detection range rate, the first range-rate window size, and the second range-rate window size;

in accordance with a determination that the first range-rate window index satisfies a predefined criterion, calculating, using the processing circuitry, an estimated range-rate based on the first range-rate window index difference; and

in accordance with a determination that the first range-rate window index does not satisfy the predefined criteria, forgoing calculation of the estimated range-rates based on the first range-rate window index difference.

2. The method of clause 1, wherein the first range-rate window index difference is an integer.

3. The method according to

item

1 or 2, wherein,

the first transmission signal includes a first plurality of pulses having a first transmitter frequency and a first pulse repetition frequency, the first range rate window size corresponds to the first transmitter frequency and the first pulse repetition frequency, an

The second transmission signal includes a second plurality of pulses having a second transmitter frequency and a second pulse repetition frequency different from the first pulse repetition frequency, the second range rate window size corresponding to the second transmitter frequency and the second pulse repetition frequency.

4. The method of any of items 1-3, wherein the method is performed according to an equation

To calculate the first range-rate window index, where N₁Is the index of the first range-rate window,

is the first rate of detection of the rate of change,

5. The method of any of items 1-4, wherein the method is performed according to an equation

To calculate the estimated range-rate, where v_{r_est}Is the rate at which the estimates are made,

is the second detection range rate, N₂＝N₁+N_diff，N₁Is the first range-rate window index, N_diffIs the first range-rate window index difference, and vun₂Is the second range-rate window size.

6. The method of any of items 1-5, further comprising:

in accordance with a determination that the first range-rate window index does not satisfy the predefined criteria, calculating, using the processing circuitry, a second range-rate window index based on a second range-rate window index difference; and

in accordance with a determination that the second range-rate window index satisfies the predefined criteria, estimated range-rates are calculated, using the processing circuitry, based on the second range-rate window index differences.

7. The method of any of clauses 1-6, wherein the first range-rate window size is different than the second range-rate window size.

8. The method of any of clauses 1-7, wherein the first transmission signal has a first pulse repetition frequency and the second transmission signal has a second pulse repetition frequency that is different from the first pulse repetition frequency.

9. The method according to any one of the preceding claims 8,

wherein the first range-rate window size is based on the first pulse repetition frequency, an

Wherein the second range-rate window size is based on the second pulse repetition frequency and is different from the first range-rate window size.

10. The method of any of items 1-9, further comprising:

transmitting, using the processing circuit, the estimated range-rates to a tracking circuit.

11. The method of any of clauses 1-10, wherein the calculating of the first range-rate window index is performed in accordance with a determination that a gating criterion is satisfied, wherein the gating criterion is based on the first received signal and the second received signal.

12. The method of clause 11, wherein the gating criteria is not based on the first detection range rate or the second detection range rate.

13. The method of any of items 1-12, further comprising:

in accordance with a determination that the estimated range-rates and the estimated range-rates of the third received signals satisfy a matching criterion, correlating the detection results of the second received signal with the detection results of the third received signal.

14. The method of any of items 1-13, wherein the estimated range-rates are second estimated range-rates, the method further comprising:

determining a predicted distance based on a first detected distance based on the first received signal and a first estimated range rate;

determining a second detection distance based on the second received signal; and

in accordance with a determination that a difference between the predicted range and the second detected range exceeds a range threshold, forgoing associating the second received signal with the first received signal.

15. The method of any of items 1-14, further comprising:

in accordance with a determination that the first range-rate window index satisfies the predefined criteria and the first range-rate window index difference is not zero, indicating that the estimated range-rates are based on non-zero range-rate window index differences.

16. The method of any of items 1-15, wherein receiving the first receive signal is performed with a first receiver of the one or more receivers and receiving the second receive signal is performed with a second receiver of the one or more receivers different from the first receiver.

17. The method of any of items 1-15, wherein receiving the first receive signal and receiving the second receive signal are performed with a same receiver of the one or more receivers.

18. The method of any of items 1-17, further comprising:

using a control circuit, controlling the autonomous vehicle using the estimated range-rate.

19. A system, comprising:

one or more computer processors; and

one or more storage media storing instructions that, when executed by the one or more computer processors, cause performance of the method of any of items 1-18.

20. A storage medium storing instructions that, when executed by one or more computing devices, cause performance of the method recited in any of items 1-18.

21. An apparatus comprising means for performing the method recited in any one of claims 1-18.

Cross Reference to Related Applications

The present application claims the benefit of U.S. provisional patent application No.62/965,409 entitled "resolution RANGE RATE AMBIGUITY IN SENSOR responses" filed 24/1/2020, the entire contents of which are incorporated herein by reference.

Claims

1. A method, comprising:

2. The method of claim 1, wherein the first range-rate window index difference is an integer.

3. The method of claim 1, wherein,

4. The method of claim 1, wherein the method is performed according to the equation

is the first rate of detection of the rate of change,

5. The method of any of claims 1-4, wherein the method is performed according to an equation

6. The method of claim 1, further comprising:

7. The method of claim 1, wherein the first range-rate window size is different from the second range-rate window size.

8. The method of claim 1, wherein the first transmission signal has a first pulse repetition frequency and the second transmission signal has a second pulse repetition frequency different from the first pulse repetition frequency.

9. The method of claim 8, wherein the first and second light sources are selected from the group consisting of,

10. The method of claim 1, further comprising:

11. The method of claim 1, wherein the calculation of the first range-rate window index is performed in accordance with a gating criterion determined to be satisfied, wherein the gating criterion is based on the first received signal and the second received signal.

12. The method of claim 11, wherein the gating criteria is not based on the first rate of detection or the second rate of detection.

13. The method of claim 1, further comprising:

14. The method of any of claims 1, 6-13, wherein the estimated range rates are second estimated range rates, the method further comprising:

15. The method of claim 1, further comprising:

16. The method of claim 1, wherein receiving the first receive signal is performed with a first receiver of the one or more receivers and receiving the second receive signal is performed with a second receiver of the one or more receivers different from the first receiver.

17. The method of claim 1, wherein receiving the first receive signal and receiving the second receive signal are performed with a same receiver of the one or more receivers.

18. The method of claim 1, further comprising:

19. A system, comprising:

one or more computer processors; and

one or more storage media storing instructions that, when executed by the one or more computer processors, cause performance of the method recited in any of claims 1-18.

20. A storage medium storing instructions that, when executed by one or more computing devices, cause performance of the method recited in any one of claims 1-18.